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Dr. K. Sailaja Kumar is an Associate Professor in the School of Computer Science and Engineering at RV University, Bengaluru, Karnataka, India. Her academic interests include Artificial Intelligence, Machine Learning, intelligent transportation systems, data analytics, and smart computing technologies. She is actively engaged in research, teaching, and mentoring students in advanced computing disciplines.
The swift expansion of urban areas, rising automobile ownership, and the development of transportation infrastructures have posed considerable difficulties in traffic regulation, route efficiency, and urban mobility. Traditional shortest path algorithms typically depend on fixed road networks and neglect to account for dynamic elements like traffic congestion, road closures, weather conditions, accidents, construction projects, and variations in travel demand. The amalgamation of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Global Positioning System (GPS), Cloud Computing, Edge Computing, and Intelligent Transportation Systems (ITS) has revolutionised route planning through the facilitation of real-time shortest path forecasting and smart navigation. AI-powered routing systems perpetually assess traffic dynamics, vehicular motion, roadway conditions, ecological information, and past travel trends to suggest the most optimal paths, thereby reducing travel duration, fuel usage, and congestion. This chapter elucidates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues pertaining to AI-enhanced shortest path forecasting for Intelligent Transportation Systems.